This relates to graphics processing and particularly to the graphical depiction of motion blur and depth of field.
Motion blur is an important visual effect that reduces temporal aliasing and makes moving content appear smoother. However, efficient rendering of motion blur in real-time three-dimensional graphics is nontrivial. In stochastic rasterization of motion blur, moving geometric primitives are sampled in both space and time, using a large set of samples to obtain high-quality motion-blurred images with low noise levels. For each of the sample positions, an inside test is executed to determine whether the moving primitive covers the sample. This overlap test in x, y and t space is generally more expensive than an inside test in traditional rasterizers.
Depth of field is an effect stemming from the finite aperture of a real camera, and makes out-of-focus objects appear blurry. In stochastic rasterization of depth of field, geometric primitives are sampled in both space and over the camera lens.
A stochastic rasterizer that supports simultaneous motion blur and depth of field executes visibility tests for each triangle in a five dimensional (5D) domain (x,y,u,v,t). This domain consists of spatial positions (x,y), lens coordinates (u,v) and time t. These visibility tests are computationally much more expensive than a single visibility test in standard 2D rasterization. Additionally, for each triangle, many (e.g. 16-64) such tests are executed per pixel to obtain images with low noise levels.